In todays complex society many companies have large and complex workflows which may involve several different tools. This report will look into the workflow of a big programming company, from when a mistake is found in the code until it has been corrected and see how the workflow can be improved. / <p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet</p>

The focus of the study is to develop a novel framework of workflow technology adoption (WAF) for public policy making. Workflow technology (WfT) has emerged to support both technological and economic importance for private and public organisations to meet competitive business and computational environment. Despite all the benefits of WfT, there has been limited adoption by policy makers. One of the reasons for this is the lack of a theoretical framework for researchers and a tool for decision makers that help in evaluating WfT adoption. Due to the limited knowledge on workflow adoption, from the socio-technical and organisational side, an academic challenge is to identify the key factors that influence adoption. To overcome these limitations, the proposed workflow adoption framework is based on the amalgamation of Technology, Organisation, Environment and Task Technology Fit theories that is evaluated and extended by using a qualitative multiple case study approach. Qualitative content analysis of the empirical data collected from three UK local government authorities resulted in a novel framework for WfT adoption, particularly for the policy making context, that has 17 factors, which influence adoption decisions. Findings revealed that attitude, organisation structure and competition did not have any influential role due to immense managerial support, constructive communication and thorough training provided to employees. Also, competition as an environmental factor had no influence over the adoption of WfT, since public services do not compete like in the private sector over market share, instead imitative pressure emerged due to close collaboration with neighbouring LGAs. It was also found that stakeholders and timescale had influence on the decisions and collection proved to be an important task characteristic of the LGAs. The major contribution of the framework is that it provides an understanding of the phenomenon by identifying key factors from the contextual perspective (i) technological set (complexity, relative advantage, compatibility and timescale), (ii) organisational set (managerial support, cost, information intensity and stakeholders), (iii) environmental set (government regulations and imitative pressure). In addition, it identifies functional fit of WfT with the task requirements by classifying functions (communication, information processing and process structuring) and policy making task characteristics (generation, choice, collection and combination) and then analysing the fit. Together it provides a holistic understanding of the phenomenon that aids informed decision making relevant to WfT adoption. Although, the literature has emphasised the technical aspects so far, this study contributes to future studies through organisational and managerial knowledge, classifying WfT’s functions and policy making task characteristics. Practical implications exist for the policy makers who can adopt the WAF as a recommendation tool to make informed decisions and achieve competitive advantage. Keywords: Workflow technology, IT adoption, decision making, policy making process and e-government.

No description available.

Tese de mestrado integrado. Engenharia Informática e Computação. Universidade do Porto. Faculdade de Engenharia. 2010

The context-workflow relationship is often poorly defined or forgotten entirely. In workflow systems and applications context is either omitted, defined by the workflow or defined based on a single aspect of a contextual dimension. In complex environments this can be problematic as the definition of context is useful in determining the set of possible workflows. Context provides the envelope that surrounds the workflow and determines what is or is not possible. The relationship between workflow and context is also poorly defined. That context can exist independently of workflow is often ignored, and workflow does not exist independently of context. Workflow representations void of context violate this stipulation. In order for a workflow representation to exist in a contextual dimension it must possess the same dimensions as the context. In this thesis we selected one contextual dimension to study, in this case the spatial dimension, and developed a comprehensive definition using building data models. Building data models are an advanced form of representation that build geometric data models into an ob ject-oriented representation consisting of common building elements. The building data model used was the Industry Foundation Classes (IFC) as it is the leading standard in this emerging field. IFC was created for the construction of facilities and not the use of facilities at a later time. In order to incorporate workflows into IFC models, a zoning technique was developed in order to represent the workflow in IFC. The zoning concept was derived from multi-criteria layout for facilities layout and was adapted for IFC and workflow. Based on the above work a zoning extension was created to explore the combination of IFC, workflow and simulation. The extension is a proof of concept and is not intended to represent a robust formalized system. The results indicate that the use of a comprehensive definition of a contextual dimension may prove valuable to future expert systems.

The context-workflow relationship is often poorly defined or forgotten entirely. In workflow systems and applications context is either omitted, defined by the workflow or defined based on a single aspect of a contextual dimension. In complex environments this can be problematic as the definition of context is useful in determining the set of possible workflows. Context provides the envelope that surrounds the workflow and determines what is or is not possible. The relationship between workflow and context is also poorly defined. That context can exist independently of workflow is often ignored, and workflow does not exist independently of context. Workflow representations void of context violate this stipulation. In order for a workflow representation to exist in a contextual dimension it must possess the same dimensions as the context. In this thesis we selected one contextual dimension to study, in this case the spatial dimension, and developed a comprehensive definition using building data models. Building data models are an advanced form of representation that build geometric data models into an ob ject-oriented representation consisting of common building elements. The building data model used was the Industry Foundation Classes (IFC) as it is the leading standard in this emerging field. IFC was created for the construction of facilities and not the use of facilities at a later time. In order to incorporate workflows into IFC models, a zoning technique was developed in order to represent the workflow in IFC. The zoning concept was derived from multi-criteria layout for facilities layout and was adapted for IFC and workflow. Based on the above work a zoning extension was created to explore the combination of IFC, workflow and simulation. The extension is a proof of concept and is not intended to represent a robust formalized system. The results indicate that the use of a comprehensive definition of a contextual dimension may prove valuable to future expert systems.

Recent enthusiasm in grid computing has resulted in a tremendous amount of research in resource scheduling techniques for tasks in a workflow. Most of the work on resource scheduling is aimed at minimizing the total response time for the entire workflow and treats the estimated response time of a task running on a local resource as a constant. However in a dynamic environment such grid computing, the behavior of resources simply cannot be ensured. In this thesis, thus, we propose a probabilistic framework for resource scheduling in a grid environment that views the task response time as a probability distribution to take into consideration the uncertain factors. The goal is to dynamically assign resources to tasks so as to maximize the probability of completing the entire workflow within a desired total response time. We propose three algorithms for the dynamic resource scheduling in grid environment, namely the integer programming, the max-max heuristic and the min-max heuristic. Experimental results using synthetic data derived from a real protein annotation workflow application demonstrate that the proposed probability-based scheduling strategies have similar performance in an environment with homogeneous resources and perform better in an environment with heterogeneous resources, when compared with the existing methods that consider the response time as a constant. Of the two proposed heuristics, min-max generally yields better performance.

Recent enthusiasm in grid computing has resulted in a tremendous amount of research in resource scheduling techniques for tasks in a (scientific) workflow. There are many factors that may affect the scheduling results, one of which is whether the application is computing-intensive or data-intensive. Most of the grid scheduling researches focus on a single aspect of the environments. In this thesis, we base on our previous work, a probability-based framework for dynamic resource scheduling, and consider data transmission overhead in our scheduling algorithms. The goal is to dynamically assign resources to tasks so as to maximize the probability of completing the entire workflow within a desired total response time. We propose two algorithms for the dynamic resource scheduling in grid environment, namely largest deadline completion probability (LDCP) and smallest deadline completion probability (SDCP). Furthermore, considering the data transmission overhead, we propose a suite of push-based scheduling algorithms, which schedule all the immediate descendant tasks when a task is completed. These are algorithms will be compared to the pull-demand scheduling algorithms in our previous work and workflow-based algorithms proposed by other researchers. We use GridSim toolkit to model the grid environment and evaluate the performance of the various scheduling algorithms.

Passau, Universität, Thesis (doctoral), 2001.

Kassel, Universität, Thesis (doctoral), 2000.